{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('day.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c9e299390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用distplt画出直方图\n",
    "fig=plt.figure()\n",
    "sns.distplot(df['cnt'],bins=30,kde=True)\n",
    "plt.xlabel('cnt',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXB9C/qIuDAinoiInRGxul/7kSQdWl+CPiKOAf4BuAg4E7giIs7sx74kSYfWrzP6c4GdmflkZr4GrAeW9WlfkqRD6FfQzweeaVveXbVJko6wfv3P2OjQlr/UIWIMGKsWxyPiiZr7OgX4Yc11i/VB69KRdenMunR2JOoSN3W1+m9MpVO/gn43sLBteQHwbHuHzFwDrOl2RxGxJTNHut1OaaxLZ9alM+vSWSl16dfUzX8DiyNiUUQcCywH7unTviRJh9CXM/rMnIiIvwQeAI4BbsvMx/qxL0nSofVr6obMvB+4v1/bb9P19E+hrEtn1qUz69JZEXWJzJy8lyRp2vIjECSpcNM26GfaRyxExMKIeCgitkfEYxHxoar95IjYGBE7qtt5VXtExOeq+jwSEWe3bWtF1X9HRKwY1HPqpYg4JiK+FRH3VsuLImJz9RzvqC4KICKOq5Z3Vo8PtW3juqr9iYh412CeSe9ExNyI2BAR362Om7d5vEBEfLj6GXo0Im6PiOOLP14yc9p90XqB9/vA6cCxwHeAMwc9rj4/59OAs6v7bwS+R+vjJf4OWF21rwZuqu5fDHyN1nsazgM2V+0nA09Wt/Oq+/MG/fx6UJ+PAP8C3Fst3wksr+5/HviL6v77gc9X95cDd1T3z6yOo+OARdXxdcygn1eXNVkH/Fl1/1hg7kw/Xmi9cfMpYHbbcbKy9ONlup7Rz7iPWMjMvZn5cHX/x8B2WgftMlo/0FS3l1X3lwFfypZNwNyIOA14F7AxM1/MzJeAjcCFR/Cp9FxELAAuAW6tlgO4ANhQdTmwLvvrtQFYWvVfBqzPzJ9m5lPATlrH2bQUEW8C3gGsBcjM1zLzZTxeoHURyuyImAWcAOyl8ONlugb9jP6IherPx7cCm4FGZu6F1i8D4NSq28FqVGLtPgN8DPjfavnNwMuZOVEttz/HXzz/6vF9Vf/S6nI68ALwhWpK69aIOJEZfrxk5h7gk8DTtAJ+H7CVwo+X6Rr0k37EQqkiYg7wVeCvMvNHh+raoS0P0T4tRcS7geczc2t7c4euOcljRdWF1lnr2cAtmflW4BVaUzUHMyPqUr0msYzWdMuvAyfS+pTdAxV1vEzXoJ/0IxZKFBFvoBXyX87Mu6rm56o/salun6/aD1aj0mp3PnBpROyiNYV3Aa0z/LnVn+bwy8/xF8+/evwk4EXKq8tuYHdmbq6WN9AK/pl+vPwB8FRmvpCZPwPuAt5O4cfLdA36GfcRC9W84Fpge2Z+qu2he4D9V0KsAO5ua39vdTXFecC+6k/1B4B3RsS86uzmnVXbtJSZ12XmgswconUcfD0zrwQeAi6vuh1Yl/31urzqn1X78uoqi0XAYuCbR+hp9Fxm/gB4JiLOqJqWAo8zw48XWlM250XECdXP1P66lH28DPrV4LpftK4S+B6tV7uvH/R4jsDz/T1afxo+Any7+rqY1nzhg8CO6vbkqn/Q+ucv3we2ASNt23ofrRePdgJXDfq59bBGo/z/VTen0/rB2wl8BTiuaj++Wt5ZPX562/rXV/V6Arho0M+nB/X4XWBLdcz8K62rZmb88QL8DfBd4FHgn2ldOVP08eI7YyWpcNN16kaSNEUGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9Jhfs/YsPo+xbxNngAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c9e29e320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['cnt'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c9e65f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes=plt.subplots(1,2,sharey=True,figsize=(6,4))\n",
    "sns.boxplot(data=df['cnt'],ax=axes[0]);\n",
    "sns.violinplot(data=df['cnt'],ax=axes[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c9e6e8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols=df.columns\n",
    "#相关系数绝对值大于0.5认为是强相关,使用函数corr计算相关系数。\n",
    "data_corr=df.corr()\n",
    "data_corr=data_corr.abs()\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "sns.heatmap(data_corr,mask=data_corr<0.5,cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据分离\n",
    "y=df['cnt']\n",
    "x=df.drop('cnt',axis=1)\n",
    "log_y=np.log1p(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#离散量编码\n",
    "x['holiday'].astype('object')\n",
    "x_cat1=x['holiday']\n",
    "x_cat1=pd.get_dummies(x_cat1,prefix='holiday')\n",
    "x=x.drop('holiday',axis=1)\n",
    "feat_names=x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday_0</th>\n",
       "      <th>holiday_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   holiday_0  holiday_1\n",
       "0          1          0\n",
       "1          1          0\n",
       "2          1          0\n",
       "3          1          0\n",
       "4          1          0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_cat1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x['workingday'].astype('object')\n",
    "x_cat2=x['workingday']\n",
    "x_cat2=pd.get_dummies(x_cat2,prefix='workingday')\n",
    "x=x.drop('workingday',axis=1)\n",
    "feat_names=x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workingday_0</th>\n",
       "      <th>workingday_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   workingday_0  workingday_1\n",
       "0             1             0\n",
       "1             1             0\n",
       "2             0             1\n",
       "3             0             1\n",
       "4             0             1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_cat2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x['dteday'].astype('object')\n",
    "x_cat3=x['dteday']\n",
    "x_cat3=pd.get_dummies(x_cat3,prefix='dteday')\n",
    "x=x.drop('dteday',axis=1)\n",
    "feat_names=x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   dteday_2011-01-01  dteday_2011-01-02  dteday_2011-01-03  dteday_2011-01-04  \\\n",
       "0                  1                  0                  0                  0   \n",
       "1                  0                  1                  0                  0   \n",
       "2                  0                  0                  1                  0   \n",
       "3                  0                  0                  0                  1   \n",
       "4                  0                  0                  0                  0   \n",
       "\n",
       "   dteday_2011-01-05  dteday_2011-01-06  dteday_2011-01-07  dteday_2011-01-08  \\\n",
       "0                  0                  0                  0                  0   \n",
       "1                  0                  0                  0                  0   \n",
       "2                  0                  0                  0                  0   \n",
       "3                  0                  0                  0                  0   \n",
       "4                  1                  0                  0                  0   \n",
       "\n",
       "   dteday_2011-01-09  dteday_2011-01-10        ...          dteday_2012-12-22  \\\n",
       "0                  0                  0        ...                          0   \n",
       "1                  0                  0        ...                          0   \n",
       "2                  0                  0        ...                          0   \n",
       "3                  0                  0        ...                          0   \n",
       "4                  0                  0        ...                          0   \n",
       "\n",
       "   dteday_2012-12-23  dteday_2012-12-24  dteday_2012-12-25  dteday_2012-12-26  \\\n",
       "0                  0                  0                  0                  0   \n",
       "1                  0                  0                  0                  0   \n",
       "2                  0                  0                  0                  0   \n",
       "3                  0                  0                  0                  0   \n",
       "4                  0                  0                  0                  0   \n",
       "\n",
       "   dteday_2012-12-27  dteday_2012-12-28  dteday_2012-12-29  dteday_2012-12-30  \\\n",
       "0                  0                  0                  0                  0   \n",
       "1                  0                  0                  0                  0   \n",
       "2                  0                  0                  0                  0   \n",
       "3                  0                  0                  0                  0   \n",
       "4                  0                  0                  0                  0   \n",
       "\n",
       "   dteday_2012-12-31  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  0  \n",
       "\n",
       "[5 rows x 731 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_cat3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "#初始化特征值和目标值的最大最小器\n",
    "ss_x=StandardScaler()\n",
    "ss_y=StandardScaler()\n",
    "ss_log_y=StandardScaler()\n",
    "\n",
    "x=ss_x.fit_transform(x)\n",
    "y = ss_y.fit_transform(y.reshape(-1, 1))\n",
    "log_y = ss_y.fit_transform(log_y.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_data=pd.DataFrame(data=x,columns=feat_names,index=df.index)\n",
    "fe_data=pd.concat([fe_data,x_cat1,x_cat2,x_cat3],axis=1,ignore_index=False)\n",
    "\n",
    "fe_data['cnt']=y\n",
    "fe_data['log_cnt']=log_y\n",
    "\n",
    "fe_data.to_csv('FE_sharingbike.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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